Deepak Nagaraj
Supreeth Mysore Venkatesh
ABSTRACT
The tight financial situation in many municipalities does not allow them to record and evaluate the condition of their own transport infrastructure in detail. The present rule-based road quality estimation methods are outdated, very expensive and less accurate. The only subjective and non-recurring documentation leads to the fact that there is no resilient data basis for intelligent, data-based condition forecasts, which would actually be possible with methods of artificial intelligence (AI) and machine learning (ML). The considerable potential for cost minimization that such forecasts would open up via maintenance optimization remains untapped. In this research work, we demonstrate a road quality estimation system given the Inertial Measurement Unit (IMU) data from smartphone mounted on a vehicle. The system consists of a data preprocessing pipeline which removes many uncertainties along with more accurate geo-referencing of the raw data, and training a machine learning model to estimate road quality in terms of a continuous variable. Route quality information is gathered together with GPS tracking using the IMU data coming from smartphone mounted on a vehicle. The ground-truth (road quality) is obtained using conventional road quality measurement system. Next, distinctive features are obtained from the IMU raw data. Consequently, a machine learning model is trained to estimate the road quality from the obtained features with high performance.
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Index Terms
- A Practical Approach for Road Quality Estimation using Smartphone based Inertial Data: IMU data processing pipeline to estimate road quality
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